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Collaborative filtering recommender systems taxonomy

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Abstract

In the era of internet access, recommender systems try to alleviate the difficulty that consumers face while trying to find items (e.g., services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of collaborative filtering recommend systems. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a collaborative filtering recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of collaborative filtering recommender systems according to their ability to efficiently handle well-known drawbacks.

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Notes

  1. Movie Lens Datasets available at: http://grouplens.org/datasets/movielens/1m/.

  2. Pinterest Dataset available at: https://sites.google.com/site/xueatalphabeta/academic-projects.

  3. Yelp Dataset available at: https://github.com/hexiangnan/sigir16-eals.

  4. Gowalla dataset available at: http://dawenl.github.io/data/gowalla pro.zip.

  5. Million song dataset available at: https://labrosa.ee.columbia.edu/millionsong/.

  6. Yahoo! Webscope R4 dataset available at: http://webscope.sandbox.yahoo.com.

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Acknowledgements

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code: T1EDK-02147).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71004, Heraklion, Crete, Greece

    Harris Papadakis, Antonis Papagrigoriou, Eleftherios Kosmas & Paraskevi Fragopoulou

  2. Department of Management Science and Technology, Hellenic Mediterranean University, 72100, Agios Nikolaos, Crete, Greece

    Costas Panagiotakis

  3. Foundation for Research and Technology-Hellas (FORTH), Institute of Computer Science, 70013, Heraklion, Crete, Greece

    Costas Panagiotakis & Paraskevi Fragopoulou

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  1. Harris Papadakis
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  2. Antonis Papagrigoriou
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  3. Costas Panagiotakis
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  4. Eleftherios Kosmas
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  5. Paraskevi Fragopoulou
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Correspondence to Harris Papadakis.

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Papadakis, H., Papagrigoriou, A., Panagiotakis, C. et al. Collaborative filtering recommender systems taxonomy. Knowl Inf Syst 64, 35–74 (2022). https://doi.org/10.1007/s10115-021-01628-7

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